Control of Uncertain T-S Fuzzy Time-Delay System: A Delay Decomposition Approach

This paper is concerned with the problem of robust ∞ control for a class of uncertain time-delay fuzzy systems with normbounded parameter uncertainties. By utilizing the instrumental idea of delay decomposition, the decomposed Lyapunov-Krasovskii functional is introduced to uncertain T-S fuzzy system, and some delay-dependent conditions for the existence of robust controller are formulated in the form of linear matrix inequalities (LMIs). When these LMIs are feasible, a controller is presented. A numerical example is given to demonstrate the effectiveness of the proposed method

The estimation of crop emergence in potatoes by UAV RGB imagery

Abstract Background Crop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. Results In this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient ($$r^{2}$$ r2 ) of 0.96 and provide an efficient tool to evaluate emergence uniformity. Conclusions The proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency

Robust H

This paper is concerned with the problem of robust H∞ control for a class of uncertain time-delay fuzzy systems with norm-bounded parameter uncertainties. By utilizing the instrumental idea of delay decomposition, the decomposed Lyapunov-Krasovskii functional is introduced to uncertain T-S fuzzy system, and some delay-dependent conditions for the existence of robust controller are formulated in the form of linear matrix inequalities (LMIs). When these LMIs are feasible, a controller is presented. A numerical example is given to demonstrate the effectiveness of the proposed method

Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales

Ambient exposure to particulate matter (PM) air pollution is known to have an adverse effect on public health worldwide. Rapid increase rates of economic and urbanization, industrial development, and environmental change in China have exacerbated the occurrence of air pollution. This study examines the temporal and spatial distribution of PM on national, regional and local scales in China during 2014–2016. The relationships between the PM2.5 concentration rising rate (PMRR) and meteorological parameters (wind speed and wind direction) are discussed. The dataset of Air Quality Index (AQI), PM10 (PM diameter < 10 μm ) and PM2.5 (PM diameter < 2.5 μm) were collected in 169, 369, and 367 cities in 2014, 2015, and 2016 over China, respectively. The results show that the air quality has been generally improved on the national scale, but deteriorated locally in areas such as the Feiwei Plain. The northwest China (NW) and Beijing-Tianjin-Hebei (BTH) regions are the worst areas of PM pollution, which are mainly manifested by the excessive PM10 caused by blowing dust in spring in NW and the intensive emissions of PM2.5 in winter in BTH. With the classified seven geographic regions, we demonstrate the significant spatial difference and seasonal variation of PM concentration and PM2.5/PM10 ratio, which indicate different emission sources. Furthermore, the dynamic analysis of the PM2.5 pollution process in 11 large urban cities shows dramatic effects of wind speed and wind direction on the PM2.5 loadings

Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging

© 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management